TY - GEN
T1 - Applying a self-organizing map to sensor-array characterization
AU - Lemos, R. A.
AU - Nakamura, M.
AU - Kuwano, H.
PY - 1993/12/1
Y1 - 1993/12/1
N2 - As a basic application of neural networks, the authors implemented a self-organizing map (SOM) as an algorithm to classify the response vectors from a sensor array exposed to various chemical vapors. Our chemical sensing system consists of an array of piezoelectric quartz-crystal microbalance (QCM) sensors, each coated with a different polymer membrane. Typically, statistical analysis are employed to characterize the sensor response to various gases and to classify each individual gas. However, because the sorption-desorption cycle can require a long time to come to equilibrium, the initial vectors do not contain much unique information. We replaced principal-component analysis with the self-organizing map as a visual method of finding the time at which the sensor-array signals become unique and of estimating the quality of the extracted features. In addition, we found that the SOM can accurately classify response vectors faster than principal-component analysis.
AB - As a basic application of neural networks, the authors implemented a self-organizing map (SOM) as an algorithm to classify the response vectors from a sensor array exposed to various chemical vapors. Our chemical sensing system consists of an array of piezoelectric quartz-crystal microbalance (QCM) sensors, each coated with a different polymer membrane. Typically, statistical analysis are employed to characterize the sensor response to various gases and to classify each individual gas. However, because the sorption-desorption cycle can require a long time to come to equilibrium, the initial vectors do not contain much unique information. We replaced principal-component analysis with the self-organizing map as a visual method of finding the time at which the sensor-array signals become unique and of estimating the quality of the extracted features. In addition, we found that the SOM can accurately classify response vectors faster than principal-component analysis.
UR - http://www.scopus.com/inward/record.url?scp=0027879348&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027879348&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027879348
SN - 0780314212
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2009
EP - 2012
BT - Proceedings of the International Joint Conference on Neural Networks
A2 - Anon, null
PB - Publ by IEEE
T2 - Proceedings of 1993 International Joint Conference on Neural Networks. Part 2 (of 3)
Y2 - 25 October 1993 through 29 October 1993
ER -